From 9db4afa06e0e6489e5d452c9adf3c7c1a22cfd8c Mon Sep 17 00:00:00 2001 From: Marcel Frommelt Date: Fri, 18 Jun 2021 21:57:52 +0900 Subject: [PATCH] added version with support for PCs with RAM between 16 GB and 64 GB --- main_LOW_RAM.py | 1089 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1089 insertions(+) create mode 100644 main_LOW_RAM.py diff --git a/main_LOW_RAM.py b/main_LOW_RAM.py new file mode 100644 index 0000000..d756747 --- /dev/null +++ b/main_LOW_RAM.py @@ -0,0 +1,1089 @@ +import sys +import pickle +from pyfiglet import Figlet +import warnings +import numpy as np +import xarray as xr +import pandas as pd +import pickle as pkl +import cartopy.crs as ccrs +import astropy.units as unit +import matplotlib.pyplot as plt +import dask.array as da +from dask import delayed +from dask.diagnostics import ProgressBar +from datetime import datetime +starttime = datetime.now() +print('----------------------------------------') +ascii_banner = Figlet(font="slant") +print(ascii_banner.renderText("BASTET")) +print("Ver. 1.0, 2021 by Marcel Frommelt") +print('----------------------------------------') +print("") +print("") + +from netCDF4 import Dataset +from astropy.time import Time +from scipy import interpolate +from scipy.spatial import cKDTree +from scipy.integrate import solve_ivp +from input.user_input import * +from input.natural_constants import * +from models.gravity import grav +from models.valving import valving +from models.ballasting import ballasting +from dask.diagnostics import ProgressBar +from models.simple_atmosphere import T_air_simple, p_air_simple, rho_air_simple +from models.sun import sun_angles_analytical, tau +from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_PalumboMC, cd_sphere +from models.transformation import visible_cells, transform, radii, transform2 +from multiprocessing import Process + +starttime = datetime.now() + +if not sys.warnoptions: + warnings.simplefilter("ignore") + +data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Data_PoGo2016.xls', sheet_name='SuperTIGER2') # Tabelle3 + +comp_time = pd.DataFrame(data, columns=['Time']).to_numpy().squeeze() +comp_height = pd.DataFrame(data, columns=['Height']).to_numpy().squeeze() +comp_lat = pd.DataFrame(data, columns=['Latitude']).to_numpy().squeeze() +comp_lon = pd.DataFrame(data, columns=['Longitude']).to_numpy().squeeze() + +print("") +print("INITIALISING SIMULATION...") +print("") +print("Launch location:") +print("longitude: %.4f deg" % (start_lon)) +print("latitude: %.4f deg" % (start_lat)) +print("Launch time: " + str(start_utc) + " (UTC)") +print("") +print("Reading ERA5-datasets, please wait.") + +float_data = Dataset(ERA5_float, "r", format="NETCDF4") + +# ERA5 MULTI-LEVEL FLOAT (WIND + ATMOSPHERIC DATA DURING FLOAT) + +ERAtime = float_data.variables['time'][:] # time +ERAlat1 = float_data.variables['latitude'][:] # latitude [deg] +ERAlon1 = float_data.variables['longitude'][:] # longitude [deg] +ERAz_float = da.from_array(float_data.variables['z']) # geopotential [m^-2/s^-2] to geopotential height [m] +ERApress_float = da.from_array(float_data.variables['level']) # pressure level [-] +ERAtemp_float = da.from_array(float_data.variables['t']) # air temperature in [K] +vw_x_float = da.from_array(float_data.variables['u']) # v_x in [m/s] +vw_y_float = da.from_array(float_data.variables['v']) # v_y in [m/s] +vw_z_float = da.from_array(float_data.variables['w']) # v_z in [m/s] + +single_data = Dataset(ERA5_single, "r", format="NETCDF4") + + +# ERA5 SINGLE-LEVEL (RADIATIVE ENVIRONMENT) + +ERAlat2 = single_data.variables['latitude'][:] # latitude [deg] +ERAlon2 = single_data.variables['longitude'][:] # longitude [deg] +ERAtcc = single_data.variables['tcc'][:] # total cloud cover [-] +ERAskt = single_data.variables['skt'][:] # skin (ground) temperature in [K] +ERAcbh = single_data.variables['cbh'][:] # cloud base height in [m] +ERAlcc = single_data.variables['lcc'][:] # low cloud cover [-] +ERAmcc = single_data.variables['mcc'][:] # medium cloud cover [-] +ERAhcc = single_data.variables['hcc'][:] # high cloud cover [-] +ERAssr = single_data.variables['ssr'][:] # hourly accumulated surface net solar radiation [J/m^2] +ERAstrn = single_data.variables['str'][:] # hourly accumulated surface net thermal radiation [J/m^2] +ERAstrd = single_data.variables['strd'][:] # hourly accumulated surface thermal radiation downwards [J/m^2] +ERAssrd = single_data.variables['ssrd'][:] # hourly accumulated surface solar radiation downwards [J/m^2] +ERAtsr = single_data.variables['tsr'][:] # hourly accumulated top net solar radiation [J/m^2] +ERAttr = single_data.variables['ttr'][:] # hourly accumulated top net thermal radiation [J/m^2] +ERAtisr = single_data.variables['tisr'][:] # hourly accumulated TOA incident solar radiation [J/m^2] +ERAstrdc = single_data.variables['strdc'][:] # hourly accumulated surface thermal radiation downward clear-sky [J/m^2] +ERAsp = single_data.variables['sp'][:] # surface pressure in [Pa] + +single_data.close() + +ascent_data = Dataset(ERA5_ascent, "r", format="NETCDF4") + + +# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT) + +ERAlat0 = ascent_data.variables['latitude'][:] # latitude [deg] +ERAlon0 = ascent_data.variables['longitude'][:] # longitude [deg] +ERAz_ascent = ascent_data.variables['z'][:] # geopotential [m^-2/s^-2] to geopotential height [m] +ERAz_ascentda = da.from_array(ascent_data.variables['z']) +ERApress_ascent = ascent_data.variables['level'][:] # pressure level [-] +ERAtemp_ascent = ascent_data.variables['t'][:] # air temperature in K +vw_x_ascent = ascent_data.variables['u'][:] # v_x in [m/s] +vw_y_ascent = ascent_data.variables['v'][:] # v_y in [m/s] +vw_z_ascent = ascent_data.variables['w'][:] # v_z in [m/s] + +# ascent_data.close() + +print("Finished reading ERA5-datasets.") + +lon_era2d0, lat_era2d0 = np.meshgrid(ERAlon0, ERAlat0) +lon_era2d1, lat_era2d1 = np.meshgrid(ERAlon1, ERAlat1) +lon_era2d2, lat_era2d2 = np.meshgrid(ERAlon2, ERAlat2) + +xs0, ys0, zs0 = transform(lon_era2d0.flatten(), lat_era2d0.flatten()) +xs1, ys1, zs1 = transform(lon_era2d1.flatten(), lat_era2d1.flatten()) +xs2, ys2, zs2 = transform(lon_era2d2.flatten(), lat_era2d2.flatten()) + +print("") +tree0 = cKDTree(np.column_stack((xs0, ys0, zs0))) +tree1 = cKDTree(np.column_stack((xs1, ys1, zs1))) +tree2 = cKDTree(np.column_stack((xs2, ys2, zs2))) +print("Built kd-trees.") +print("") + +wflag1, wflag2, wflag3, wflag4, wflag5, wflag6, wflag7, wflag8, wflag9, wflag10, wflag11, wflag12, wflag13, wflag14, wflag15, wflag16, wflag17, wflag18, wflag19 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + +flag_arr = np.zeros(20) + +def ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr): + t_epoch = deltaT_ERA + t / 3600 + + t_pre = int(t_epoch) + t_pre_ind = t_pre - int(ERAtime[0]) + t_post_ind = t_pre_ind + 1 + + xt, yt, zt = transform(lon, lat) # current coordinates + + d0, inds0 = tree0.query(np.column_stack((xt, yt, zt)), k=4) + d1, inds1 = tree1.query(np.column_stack((xt, yt, zt)), k=4) # longitude, latitude + d2, inds2 = tree2.query(np.column_stack((xt, yt, zt)), k=visible_cells(h)) # longitude, latitude visible_cells(h) + + w0 = 1.0 / d0[0] + w1 = 1.0 / d1[0] + w2 = 1.0 / d2[0] ** 2 + + lat_ind0 = np.unravel_index(inds0[0], lon_era2d0.shape)[0] + lon_ind0 = np.unravel_index(inds0[0], lon_era2d0.shape)[1] + lat_ind1 = np.unravel_index(inds1[0], lon_era2d1.shape)[0] + lon_ind1 = np.unravel_index(inds1[0], lon_era2d1.shape)[1] + lat_ind2 = np.unravel_index(inds2[0], lon_era2d2.shape)[0] + lon_ind2 = np.unravel_index(inds2[0], lon_era2d2.shape)[1] + + if h >= 30000: + try: + interp4d_temp_pre = np.ma.dot(w1, ERAtemp_float.vindex[t_pre_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_temp_post = np.ma.dot(w1, ERAtemp_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre + + interp4d_height_pre = np.ma.dot(w1, ERAz_float.vindex[t_pre_ind, :, lat_ind1, lon_ind1].compute() / g) / np.sum(w1) + interp4d_height_post = np.ma.dot(w1, ERAz_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute() / g) / np.sum(w1) + interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre + + interp4d_vw_x_pre = np.ma.dot(w1, vw_x_float.vindex[t_pre_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_x_post = np.ma.dot(w1, vw_x_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre + + interp4d_vw_y_pre = np.ma.dot(w1, vw_y_float.vindex[t_pre_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_y_post = np.ma.dot(w1, vw_y_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre + + interp4d_vw_z_pre = np.ma.dot(w1, vw_z_float.vindex[t_pre_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_z_post = np.ma.dot(w1, vw_z_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1) + interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre + + pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20, 30]) # !!! + + pressure = 100 * pressure_hPa + + temp_interp1d = interpolate.interp1d(interp4d_height, interp4d_temp) + press_interp1d = interpolate.interp1d(interp4d_height, pressure) + vw_x_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_x) + vw_y_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_y) + vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z) + + except IndexError: + if flag_arr[18] == 0: + print("Error: Please check time range of ERA5 data!") + flag_arr[18] = 1 + else: + flag_arr[18] = 1 + + elif np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0: + try: + interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre + + interp4d_height_pre = np.ma.dot(w0, ERAz_ascent[t_pre_ind, :, lat_ind0, lon_ind0] / g) / np.sum(w0) + interp4d_height_post = np.ma.dot(w0, ERAz_ascent[t_post_ind, :, lat_ind0, lon_ind0] / g) / np.sum(w0) + interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre + + interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre + + interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre + + interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) + interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre + + pressure_hPa = np.array( + [1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, + 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000]) + + pressure = 100 * pressure_hPa + + temp_interp1d = interpolate.interp1d(interp4d_height, interp4d_temp) + press_interp1d = interpolate.interp1d(interp4d_height, pressure) + vw_x_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_x) + vw_y_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_y) + vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z) + + except IndexError: + if flag_arr[19] == 0: + print("Error: Check time range of ERA5 data!") + flag_arr[19] = 1 + else: + flag_arr[19] = 1 + + else: + pass + + tcc_pre = np.ma.dot(w2, ERAtcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tcc_post = np.ma.dot(w2, ERAtcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tcc = (tcc_post - tcc_pre) * (t_epoch - t_pre) + tcc_pre + + if isinstance(tcc, float) != True: + if flag_arr[1] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"tcc\"!") + print("Assuming simplified value for parameter \"tcc\".") + flag_arr[1] = 1 + else: + flag_arr[1] = 1 + + tcc = cc + + cbh_pre = np.ma.dot(w2, ERAcbh[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + cbh_post = np.ma.dot(w2, ERAcbh[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + cbh = (cbh_post - cbh_pre) * (t_epoch - t_pre) + cbh_pre + + if isinstance(tcc, float) != True: + if flag_arr[2] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"cbh\"!") + print("Assuming simplified value for parameter \"cbh\".") + flag_arr[2] = 1 + else: + flag_arr[2] = 1 + + cbh = 2000 + + lcc_pre = np.ma.dot(w2, ERAlcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + lcc_post = np.ma.dot(w2, ERAlcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + lcc = (lcc_post - lcc_pre) * (t_epoch - t_pre) + lcc_pre + + if isinstance(lcc, float) != True: + if flag_arr[3] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"lcc\"!") + print("Assuming simplified value for parameter \"lcc\".") + flag_arr[3] = 1 + else: + flag_arr[3] = 1 + + lcc = cc/3 + + mcc_pre = np.ma.dot(w2, ERAmcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + mcc_post = np.ma.dot(w2, ERAmcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + mcc = (mcc_post - mcc_pre) * (t_epoch - t_pre) + mcc_pre + + if isinstance(mcc, float) != True: + if flag_arr[4] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"mcc\"!") + print("Assuming simplified value for parameter \"mcc\".") + flag_arr[4] = 1 + else: + flag_arr[4] = 1 + + mcc = cc/3 + + hcc_pre = np.ma.dot(w2, ERAhcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + hcc_post = np.ma.dot(w2, ERAhcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + hcc = (hcc_post - hcc_pre) * (t_epoch - t_pre) + hcc_pre + + if isinstance(hcc, float) != True: + if flag_arr[5] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"hcc\"!") + print("Assuming simplified value for parameter \"hcc\".") + flag_arr[5] = 1 + else: + flag_arr[5] = 1 + + hcc = cc/3 + + ssr_pre = np.ma.dot(w2, ERAssr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ssr_post = np.ma.dot(w2, ERAssr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ssr = ((ssr_post - ssr_pre) * (t_epoch - t_pre) + ssr_pre) / 3600 + + strn_pre = np.ma.dot(w2, ERAstrn[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strn_post = np.ma.dot(w2, ERAstrn[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strn = ((strn_post - strn_pre) * (t_epoch - t_pre) + strn_pre) / 3600 + + if isinstance(strn, float) != True: + if flag_arr[6] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"strn\"!") + print("Assuming simplified value for parameter \"strn\".") + flag_arr[6] = 1 + else: + flag_arr[6] = 1 + + strn = 0 + + skt_pre = np.ma.dot(w2, ERAskt[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + skt_post = np.ma.dot(w2, ERAskt[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + skt = ((skt_post - skt_pre) * (t_epoch - t_pre) + skt_pre) + + if isinstance(skt, float) != True: + if flag_arr[7] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"skt\"!") + print("Assuming simplified value for parameter \"skt\".") + flag_arr[7] = 1 + else: + flag_arr[7] = 1 + + skt = T_ground + + strd_pre = np.ma.dot(w2, ERAstrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strd_post = np.ma.dot(w2, ERAstrd[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strd = ((strd_post - strd_pre) * (t_epoch - t_pre) + strd_pre) / 3600 + + if isinstance(strd, float) != True: + if flag_arr[8] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"strd\"!") + print("Assuming simplified value for parameter \"strd\".") + flag_arr[8] = 1 + else: + flag_arr[8] = 1 + + strd = epsilon_ground * sigma * T_ground ** 4 + + strdc_pre = np.ma.dot(w2, ERAstrdc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strdc_post = np.ma.dot(w2, ERAstrdc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + strdc = ((strdc_post - strdc_pre) * (t_epoch - t_pre) + strdc_pre) / 3600 + + if isinstance(strdc, float) != True: + if flag_arr[9] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"strdc\"!") + print("Assuming simplified value for parameter \"strdc\".") + flag_arr[9] = 1 + else: + flag_arr[9] = 1 + + strdc = epsilon_ground * sigma * T_ground ** 4 + + ssrd_pre = np.ma.dot(w2, ERAssrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ssrd_post = np.ma.dot(w2, ERAssrd[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ssrd = ((ssrd_post - ssrd_pre) * (t_epoch - t_pre) + ssrd_pre) / 3600 + + if isinstance(ssrd, float) != True: + if flag_arr[10] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssrd\"!") + print("Assuming simplified value for parameter \"ssrd\".") + flag_arr[10] = 1 + else: + flag_arr[10] = 1 + + ssrd = 1 + ssr = 1 - Albedo + + if isinstance(ssr, float) != True: + if flag_arr[11] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssr\"!") + print("Assuming simplified value for parameter \"ssr\".") + flag_arr[11] = 1 + else: + flag_arr[11] = 1 + + ssrd = 1 + ssr = 1 - Albedo + + tsr_pre = np.ma.dot(w2, ERAtsr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tsr_post = np.ma.dot(w2, ERAtsr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tsr = ((tsr_post - tsr_pre) * (t_epoch - t_pre) + tsr_pre) / 3600 + + tisr_pre = np.ma.dot(w2, ERAtisr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tisr_post = np.ma.dot(w2, ERAtisr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + tisr = ((tisr_post - tisr_pre) * (t_epoch - t_pre) + tisr_pre) / 3600 + + if isinstance(tisr, float) != True: + if flag_arr[12] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"tisr\"!") + print("Assuming simplified value for parameter \"tisr\".") + flag_arr[12] = 1 + else: + flag_arr[12] = 1 + + utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0') + AZ, ELV = sun_angles_analytical(lat, lon, h, utc) + MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature" + TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA)) + I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2 + tisr = I_Sun * np.sin(np.deg2rad(ELV)) + + if isinstance(tsr, float) != True: + if flag_arr[13] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"tsr\"!") + print("Assuming simplified value for parameter \"tsr\".") + flag_arr[13] = 1 + else: + flag_arr[13] = 1 + + tsr = (1 - Albedo) * tisr + + ttr_pre = np.ma.dot(w2, ERAttr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ttr_post = np.ma.dot(w2, ERAttr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + ttr = ((ttr_post - ttr_pre) * (t_epoch - t_pre) + ttr_pre) / 3600 + + p0_pre = np.ma.dot(w2, ERAsp[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) + p0_post = np.ma.dot(w2, ERAsp[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2) + p0 = (p0_post - p0_pre) * (t_epoch - t_pre) + p0_pre + + if isinstance(p0, float) != True: + if flag_arr[14] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"sp\"!") + print("Assuming simplified value for parameter \"sp\".") + flag_arr[14] = 1 + else: + flag_arr[14] = 1 + p0 = 101325.0 + + if isinstance(ttr, float) != True: + if flag_arr[15] == 0: + print("WARNING: Corrupt or missing ERA5 Data for parameter \"ttr\"!") + print("Assuming simplified value for parameter \"ttr\".") + flag_arr[15] = 1 + else: + flag_arr[15] = 1 + + utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0') + AZ, ELV = sun_angles_analytical(lat, lon, h, utc) + tau_atm, tau_atmIR = tau(ELV, h, p_air, p0) + HalfConeAngle = np.arcsin(R_E / (R_E + h)) + ViewFactor = (1 - np.cos(HalfConeAngle)) / 2 + ttr = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2 + + if h > np.amax(interp4d_height): + if flag_arr[16] == 0: + print("WARNING: Balloon altitude above interpolation area!") + flag_arr[16] = 1 + else: + flag_arr[16] = 1 + + p_air = press_interp1d(np.amax(interp4d_height)) + T_air = temp_interp1d(np.amax(interp4d_height)) + u = vw_x_interp1d(np.amax(interp4d_height)) + v = vw_y_interp1d(np.amax(interp4d_height)) + w = -1 / grav(lat, h) * vw_z_interp1d(np.amax(interp4d_height)) * R_air * T_air / p_air + + elif h < np.amin(interp4d_height): + if flag_arr[17] == 0: + print("WARNING: Balloon altitude below interpolation area!") + flag_arr[17] = 1 + else: + flag_arr[17] = 1 + + p_air = press_interp1d(np.amin(interp4d_height)) + T_air = temp_interp1d(np.amin(interp4d_height)) + u = vw_x_interp1d(np.amin(interp4d_height)) + v = vw_y_interp1d(np.amin(interp4d_height)) + w = -1 / grav(lat, h) * vw_z_interp1d(np.amin(interp4d_height)) * R_air * T_air / p_air + + else: + p_air = press_interp1d(h) + T_air = temp_interp1d(h) + u = vw_x_interp1d(h) + v = vw_y_interp1d(h) + w = -1 / grav(lat, h) * vw_z_interp1d(h) * R_air * T_air / p_air + + rho_air = p_air / (R_air * T_air) + + return p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt + +t_start = Time(start_utc) + +m_gas_init = ((m_pl + m_film + m_bal_init) * (FreeLift / 100 + 1)) / (R_gas / R_air - 1) + +deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 +p_air0, p00, T_air0, rho_air0, u0, v0, w0, cbh0, tcc0, lcc0, mcc0, hcc0, ssr0, strn0, strd0, strdc0, ssrd0, tsr0, ttr0, tisr0, skt0 = ERA5Data(start_lon, start_lat, start_height, 0, deltaT_ERA, flag_arr) + + +A_top0 = np.pi/4 * 1.383 ** 2 * (m_gas_init * R_gas * T_air0 / p_air0) ** (2/3) + +y0 = [ + start_lon, # start longitude [deg] + start_lat, # start latitude [deg] + start_height, # start altitude [m] + 0, # initial v_x [m/s] + 0, # initial v_y [m/s] + 0, # initial v_z [m/s] + T_air0, # initial gas temperature [K] = initial air temperature [K] + T_air0, # initial film temperature [K] = initial air temperature [K] + m_gas_init, # initial lifting gas mass [kg] + 0, # initial factor c2 [-] + m_bal_init # initial ballast mass [kg] +] + + +t_list, h_list, v_list = [], [], [] +lat_list, lon_list = [], [] +p_list, rho_list = [], [] +Temp_list, Tgas_list, T_film_list = [], [], [] +rhog_list = [] +V_b_list = [] +Q_Albedo_list = [] +Q_IREarth_list = [] +Q_Sun_list = [] +Q_IRFilm_list = [] +Q_IRout_list = [] +Q_ConvExt_list = [] +Q_ConvInt_list = [] +utc_list = [] +ssr_list = [] +ssrd_list = [] +ttr_list = [] +strd_list = [] +strn_list = [] +tisr_list = [] +tsr_list = [] + + + +def model(t, y, m_pl, m_film, c_virt, A_top0, t_start): + utc = t_start + t * unit.second + lon = y[0] # 1 + lat = y[1] # 2 + h = y[2] # 3 + v_x = y[3] # 4 + v_y = y[4] # 5 + v_z = y[5] # 6 + T_gas = y[6] # 7 + T_film = y[7] # 8 + m_gas = y[8] # 9 + c2 = y[9] # 10 + m_bal = y[10] # 11 + + if (lon % 360) > 180: # convert longitude to value in standard interval [-180, 180] + lon = (lon % 360) - 360 + else: + lon = (lon % 360) + + if lat > 90: # set plausible limits for latitudes + lat = 90 + elif lat < -90: + lat = -90 + else: + lat = lat + + if h > 53700: + h = 53700 + elif h < 0: + h = 0 + else: + h = h + + h_list.append(h) + utc_list.append(utc) + lat_list.append(lat) + lon_list.append(lon) + Tgas_list.append(T_gas) + T_film_list.append(T_film) + + + r_lon, r_lat = radii(lat, h) # calculate radii for velocity conversion between cartesian and Earth reference frame + + deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format + + AZ, ELV = sun_angles_analytical(lat, lon, h, utc) + + MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature" + TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA)) + + I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2 + + HalfConeAngle = np.arcsin(R_E / (R_E + h)) + ViewFactor = (1 - np.cos(HalfConeAngle)) / 2 + + try: + p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr) + tau_atm, tau_atmIR = tau(ELV, h, p_air, p0) + tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0) + I_SunZ = I_Sun * tau_atm + I_Sun0 = I_Sun * tau_atm0 + except: + # in case of solver (temporarily) exceeding interpolation area (with subsequent correction by the solver itself) + # or permanent drift out of interpolation area + if h >= 30000 or (np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0): + p0 = 101325 + p_air = p_air_simple(h) + tau_atm, tau_atmIR = tau(ELV, h, p_air, p0) + tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0) + I_SunZ = I_Sun * tau_atm + I_Sun0 = I_Sun * tau_atm0 + + p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground + else: + p0 = 101325 + p_air = p_air_simple(h) + tau_atm, tau_atmIR = tau(ELV, h, p_air, p0) + tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0) + I_SunZ = I_Sun * tau_atm + I_Sun0 = I_Sun * tau_atm0 + p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground + + p_gas = p_air + + h_valve = 1.034 * V_design ** (1 / 3) + h_duct = 0.47 * h_valve + + v_relx = u - v_x # relative wind velocity x-dir (balloon frame) + v_rely = v - v_y # relative wind velocity y-dir (balloon frame) + v_relz = w - v_z # relative wind velocity z-dir (balloon frame) + + v_rel = np.sqrt(v_relx ** 2 + v_rely ** 2 + v_relz ** 2) # total relative wind velocity (balloon frame) + + alpha = np.arcsin(v_relz / v_rel) # "angle of attack": angle between longitudinal axis and rel. wind (in [rad]) + + rho_gas = p_gas / (R_gas * T_gas) # calculate gas density through *ideal* gas equation + + dP_valve = grav(lat, h) * (rho_air - rho_gas) * h_valve + dP_duct = grav(lat, h) * (rho_air - rho_gas) * h_duct + + if m_gas < 0: # limit gas mass to plausible value + m_gas = 0 + + V_b = m_gas / rho_gas # calculate balloon volume from current gas mass and gas density + rhog_list.append(rho_gas) + + if V_b > V_design: + c_duct = c_ducts + elif V_b < 0: + c_duct = 0 + V_b = 1.0 + else: + c_duct = 0 + + V_b_list.append(V_b) + + if ballasting(utc) == True: + if m_bal >= 0: + mdot = m_baldot + else: + mdot = 0 + else: + mdot = 0 + + if valving(utc) == True: # opening valve process + if c2 == 0: + c2 = 1.0 + c2dot = 0 + elif c_valve < c2 <= 1.0: + c2dot = (c_valve - 1.0) / t_open + else: + c2dot = 0 + c2 = c_valve + + if valving(utc) == False: # closing valve process + if c2 == 0: + c2dot = 0 + elif c_valve <= c2 < 1.0: + c2dot = (1.0 - c_valve) / t_close + else: + c2dot = 0 + c2 = 0 + + m_gross = m_pl + m_film + m_bal + m_tot = m_pl + m_film + m_gas + m_virt = m_tot + c_virt * rho_air * V_b + + d_b = 1.383 * V_b ** (1 / 3) # calculate diameter of balloon from its volume + L_goreB = 1.914 * V_b ** (1 / 3) + A_surf = 4.94 * V_b ** (2 / 3) + A_surf1 = 4.94 * V_design ** (2 / 3) * (1 - np.cos(np.pi * L_goreB / L_goreDesign)) + A_eff = 0.65 * A_surf + 0.35 * A_surf1 + A_top = np.pi / 4 * d_b ** 2 + A_top0 = A_top0 + + A_proj = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * np.deg2rad(ELV))) # projected area for sun radiation + A_drag = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * alpha)) # projected area for drag + + # CALCULATIONS FOR THERMAL MODEL + + if simple == True: + + q_IREarth = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR + + if ELV <= 0: + q_Albedo = 0 + else: + q_Albedo = Albedo * I_Sun * np.sin(np.deg2rad(ELV)) + + Q_Albedo = alpha_VIS * A_surf * q_Albedo * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) + Q_IREarth = alpha_IR * A_surf * q_IREarth * ViewFactor * (1 + tau_IR / (1 - r_IR)) + + q_sun = I_SunZ + + else: + + if tcc <= 0.01: + + q_IREarth1 = alpha_IR * A_surf * (strd - strn) * tau_atmIR * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) + q_IREarth2 = alpha_IR * A_surf * np.abs(ttr) * 0.5 * (1 + tau_IR / (1 - r_IR)) + # q_IREarth2 = alpha_IR * A_surf * (0.04321906 * np.abs(ttr) + 84.67820281) * (1 + tau_IR / (1 - r_IR)) + + if h > 40000: + Q_IREarth = q_IREarth2 + else: + Q_IREarth = (q_IREarth2 - q_IREarth1) / 40000 * h + q_IREarth1 + + if ELV <= 0: + q_Albedo1 = 0 + q_Albedo2 = 0 + else: + if ssrd == 0: + q_Albedo1 = 0 + else: + q_Albedo1 = alpha_VIS * A_surf * (1 - ssr / ssrd) * I_Sun0 * tau_atmIR * np.sin( + np.deg2rad(ELV)) * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) # ! + if tisr == 0: + q_Albedo2 = 0 + else: + q_Albedo2 = alpha_VIS * A_surf * (1 - tsr / tisr) * I_Sun * np.sin(np.deg2rad(ELV)) * 0.5 * ( + 1 + tau_VIS / (1 - r_VIS)) # ! + if h > 40000: + Q_Albedo = q_Albedo2 + else: + Q_Albedo = (q_Albedo2 - q_Albedo1) / 40000 * h + q_Albedo1 + + q_sun = I_SunZ + + else: + q_IRground_bc = (strd - strn) + (strd - strdc) + q_IREarth_bc = alpha_IR * A_surf * q_IRground_bc * tau_atmIR * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) + q_sun_bc = I_SunZ * (1 - tcc) + q_Albedo_bc = alpha_VIS * A_surf * (1 - ssr / ssrd) * I_Sun0 * tau_atmIR * np.sin( + np.deg2rad(ELV)) * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) + + # q_IREarth_ac = alpha_IR * A_surf * (0.04321906 * np.abs(ttr) + 84.67820281) * (1 + tau_IR / (1 - r_IR)) + q_IREarth_ac = alpha_IR * A_surf * np.abs(ttr) * 0.5 * (1 + tau_VIS / (1 - r_VIS)) + q_sun_ac = I_SunZ + q_Albedo_ac = alpha_VIS * A_surf * (1 - tsr / tisr) * I_Sun * np.sin(np.deg2rad(ELV)) * ViewFactor * ( + 1 + tau_VIS / (1 - r_VIS)) + + if h <= cbh: # "below clouds" + Q_IREarth = q_IREarth_bc + q_sun = q_sun_bc + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = q_Albedo_bc + elif h >= 12000: # "above clouds" + Q_IREarth = q_IREarth_ac + q_sun = q_sun_ac + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = q_Albedo_ac + elif h >= 6000: + if hcc >= 0.01: + Q_IREarth = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * ( + q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc + q_sun = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * ( + q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc + else: + Q_IREarth = q_IREarth_ac + q_sun = q_sun_ac + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = q_Albedo_ac + elif h >= 2000: + if mcc > 0.01 or hcc > 0.01: + Q_IREarth = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * ( + q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc + q_sun = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * ( + q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc + else: + Q_IREarth = q_IREarth_ac + q_sun = q_sun_ac + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = q_Albedo_ac + else: + Q_IREarth = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc + q_sun = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc + + if ELV <= 0: + Q_Albedo = 0 + else: + Q_Albedo = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc + + my_air = (1.458 * 10 ** -6 * T_air ** 1.5) / (T_air + 110.4) + k_air = 0.0241 * (T_air / 273.15) ** 0.9 + k_gas = 0.144 * (T_gas / 273.15) ** 0.7 + Pr_air = 0.804 - 3.25 * 10 ** (-4) * T_air + Pr_gas = 0.729 - 1.6 * 10 ** (-4) * T_gas + Gr_air = (rho_air ** 2 * grav(lat, h) * np.abs(T_film - T_air) * d_b ** 3) / (T_air * my_air ** 2) + Nu_air = 2 + 0.45 * (Gr_air * Pr_air) ** 0.25 + HC_free = Nu_air * k_air / d_b + Re = np.abs(v_rel) * d_b * rho_air / my_air + Fr = np.abs(v_rel) / np.sqrt(grav(lat, h) * d_b) + + HC_forced = k_air / d_b * (2 + 0.41 * Re ** 0.55) + HC_internal = 0.13 * k_gas * ( + (rho_gas ** 2 * grav(lat, h) * np.abs(T_film - T_gas) * Pr_gas) / (T_gas * my_air ** 2)) ** ( + 1 / 3) + HC_external = np.maximum(HC_free, HC_forced) + + Q_Sun = alpha_VIS * A_proj * q_sun * (1 + tau_VIS / (1 - r_VIS)) + Q_IRFilm = sigma * epsilon * alpha_IR * A_surf * T_film ** 4 * 1 / (1 - r_IR) + Q_IRout = sigma * epsilon * A_surf * T_film ** 4 * (1 + tau_IR / (1 - r_IR)) + Q_ConvExt = HC_external * A_eff * (T_air - T_film) + Q_ConvInt = HC_internal * A_eff * (T_film - T_gas) + + Q_Albedo_list.append(Q_Albedo) + Q_IREarth_list.append(Q_IREarth) + Q_Sun_list.append(Q_Sun) + Q_IRFilm_list.append(Q_IRFilm) + Q_IRout_list.append(Q_IRout) + Q_ConvExt_list.append(Q_ConvExt) + Q_ConvInt_list.append(Q_ConvInt) + + ssr_list.append(ssr) + ssrd_list.append(ssrd) + ttr_list.append(ttr) + strd_list.append(strd) + strn_list.append(strn) + tisr_list.append(tisr) + tsr_list.append(tsr) + + if simple == True: + c_d = c_d + else: + if drag_model == 'PalumboHigh': + c_d = cd_PalumboHigh(Fr, Re, A_top, A_top0) + elif drag_model == 'Palumbo': + c_d = cd_Palumbo(Fr, Re, A_top, A_top0) + elif drag_model == 'PalumboLow': + c_d = cd_PalumboLow(Fr, Re, A_top, A_top0) + else: + c_d = cd_sphere(Re) + + D = drag(c_d, rho_air, A_drag, v_rel) # calculate drag force + + if v_rel == 0: + Drag_x, Drag_y, Drag_z = 0, 0, 0 + else: + Drag_x, Drag_y, Drag_z = D * v_relx / v_rel, D * v_rely / v_rel, D * v_relz / v_rel + + F = grav(lat, h) * V_b * (rho_air - rho_gas) - grav(lat, h) * m_gross + Drag_z # gross inflation - weight + drag + + a_x, a_y, a_z = Drag_x / m_virt, Drag_y / m_virt, F / m_virt + + eqn1 = np.rad2deg(y[3] / r_lon) + eqn2 = np.rad2deg(y[4] / r_lat) + eqn3 = v_z + eqn4 = a_x + eqn5 = a_y + eqn6 = a_z + eqn7 = Q_ConvInt / (gamma * c_v * m_gas) - (gamma - 1) / gamma * (rho_air * grav(lat, h)) / (rho_gas * R_gas) * v_z + eqn8 = (Q_Sun + Q_Albedo + Q_IREarth + Q_IRFilm + Q_ConvExt - Q_ConvInt - Q_IRout) / (c_f * m_film) + eqn9 = -(A_ducts * c_duct * np.sqrt(np.abs(2 * dP_duct * rho_gas))) - (A_valve * c2 * np.sqrt(np.abs(2 * dP_valve * rho_gas))) + + if eqn9 > 0: + eqn9 = 0 + + eqn10 = c2dot + + if m_bal > 0: + eqn11 = -mdot + else: + eqn11 = 0 + + return [eqn1, eqn2, eqn3, eqn4, eqn5, eqn6, eqn7, eqn8, eqn9, eqn10, eqn11] + + +# DEFINITION OF EVENTS FOR SOLVER + +def at_ground(t, y, m_pl, m_film, c_virt): + return y[2] + + +def above_float(t, y, m_pl, m_film, c_virt): + return 45000 - y[2] + + +def below_float(t, y, m_pl, m_film, c_virt): + return y[2] - 30050 + + +hit_ground = lambda t, x: at_ground(t, x, m_pl, m_film, c_virt) +hit_ground.terminal = True +hit_ground.direction = -1 +excess_ascent = lambda t, x: above_float(t, x, m_pl, m_film, c_virt) +excess_ascent.terminal = True +excess_ascent.direction = -1 +instable = lambda t, x: below_float(t, x, m_pl, m_film, c_virt) +instable.terminal = True +instable.direction = -1 + + +t0 = 0 +tf = t_sim + +print("") +print("BEGINNING SIMULATION") + +sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt, A_top0, t_start), t_span=[t0, tf], y0=y0, method='RK45', events=[hit_ground, excess_ascent, instable]) #, t_eval=comp_time + +tnew = np.linspace(0, sol.t[-1], len(V_b_list)) + +print(sol.message) + + +""" +lonsol = sol.y[0, :] +latsol = sol.y[1, :] +hsol = sol.y[2, :] + +x_sol, y_sol, z_sol = transform2(lonsol, latsol, hsol) +x_test, y_test, z_test = transform2(comp_lon, comp_lat, comp_height) + +delta = ((x_sol - x_test)**2 + (y_sol - y_test)**2 + (z_sol - z_test)**2)**(0.5) + +val = 0 +i = 0 + +for x in delta: + print(latsol[i]) + print(comp_lat[i]) + print(latsol[i] - comp_lat[i]) + print(x) + val += x ** 2 + i += 1 + +RMS = np.sqrt(val/i) + +print('RMS') +print(RMS) +""" + + +print(datetime.now() - starttime) + + +arr0 = np.linspace(0, sol.t[-1], len(V_b_list)) +arr1 = np.asarray(utc_list) +arr2 = np.asarray(h_list) +arr3 = np.asarray(lat_list) +arr4 = np.asarray(lon_list) +arr5 = np.asarray(Tgas_list) +arr6 = np.asarray(T_film_list) +arr7 = np.asarray(rhog_list) +arr8 = np.asarray(V_b_list) +arr9 = np.asarray(Q_Albedo_list) +arr10 = np.asarray(Q_IREarth_list) +arr11 = np.asarray(Q_Sun_list) +arr12 = np.asarray(Q_IRFilm_list) +arr13 = np.asarray(Q_IRout_list) +arr14 = np.asarray(Q_ConvExt_list) +arr15 = np.asarray(Q_ConvInt_list) +arr16 = np.asarray(ssr_list) +arr17 = np.asarray(ssrd_list) +arr18 = np.asarray(ttr_list) +arr19 = np.asarray(strd_list) +arr20 = np.asarray(strn_list) +arr21 = np.asarray(tisr_list) +arr22 = np.asarray(tsr_list) + +ind_list = [] +for i in range(len(arr0)): + if arr0[i - 1] == arr0[i]: + ind_list.append(i) + +arr0 = np.delete(arr0, ind_list) +arr1 = np.delete(arr1, ind_list) +arr2 = np.delete(arr2, ind_list) +arr3 = np.delete(arr3, ind_list) +arr4 = np.delete(arr4, ind_list) +arr5 = np.delete(arr5, ind_list) +arr6 = np.delete(arr6, ind_list) +arr7 = np.delete(arr7, ind_list) +arr8 = np.delete(arr8, ind_list) +arr9 = np.delete(arr9, ind_list) +arr10 = np.delete(arr10, ind_list) +arr11 = np.delete(arr11, ind_list) +arr12 = np.delete(arr12, ind_list) +arr13 = np.delete(arr13, ind_list) +arr14 = np.delete(arr14, ind_list) +arr15 = np.delete(arr15, ind_list) +arr16 = np.delete(arr16, ind_list) +arr17 = np.delete(arr17, ind_list) +arr18 = np.delete(arr18, ind_list) +arr19 = np.delete(arr19, ind_list) +arr20 = np.delete(arr20, ind_list) +arr21 = np.delete(arr21, ind_list) +arr22 = np.delete(arr22, ind_list) + + + +df1 = pd.DataFrame(data={ + 'time [s]': arr0, + 'UTC': arr1, + 'Altitude [m]': arr2, + 'Latitude [deg]': arr3, + 'Longitude [deg]': arr4, + 'T_gas [K]': arr5, + 'T_film [K]': arr6, + 'rho_gas [kg/m^3]': arr7, + 'V_balloon [m^3]': arr8, + 'Q_Albedo [W/m^2]': arr9, + 'Q_IR_Earth [W/m^2]': arr10, + 'Q_Sun [W/m^2]': arr11, + 'Q_IRFilm [W/m^2]': arr12, + 'Q_IRout [W/m^2]': arr13, + 'Q_ConvExt [W/m^2]': arr14, + 'Q_ConvInt [W/m^2]': arr15, + 'SSR [W/m^2]': arr16, + 'SSRD [W/m^2]': arr17, + 'TTR [W/m^2]': arr18, + 'STRD [W/m^2]': arr19, + 'STRN [W/m^2]': arr20, + 'TISR [W/m^2]': arr21, + 'TSR [W/m^2]': arr22 +}) + +df1.to_excel("output.xlsx") + +plt.plot(sol.t, sol.y[2, :], 'k--', label='Simulation') +plt.plot(comp_time, comp_height, 'r-', label='PoGo+ Flight Test') +plt.legend() +plt.title('high factor') +plt.xlabel('time in s') +plt.ylabel('Balloon Altitude in m') +plt.show() + +plt.clf() +ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=-90)) +ax.coastlines() +ax.gridlines(draw_labels=True, linewidth=0.25, color='black') +ax.stock_img() +ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree()) + +plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic()) +plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic()) +plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic()) +# plt.savefig(os.path.join(rootdir, figname)) +plt.show() \ No newline at end of file